class_docstr: An `EventMultiplexer` manages access to multiple `EventAccumulator`s.
  
  Each `EventAccumulator` is associated with a `run`, which is a self-contained
  TensorFlow execution. The `EventMultiplexer` provides methods for extracting
  information about events from multiple `run`s.
  
  Example usage for loading specific runs from files:
  
  ```python
  x = EventMultiplexer({'run1': 'path/to/run1', 'run2': 'path/to/run2'})
  x.Reload()
  ```
  
  Example usage for loading a directory where each subdirectory is a run
  
  ```python
  (eg:) /parent/directory/path/
        /parent/directory/path/run1/
        /parent/directory/path/run1/events.out.tfevents.1001
        /parent/directory/path/run1/events.out.tfevents.1002
  
        /parent/directory/path/run2/
        /parent/directory/path/run2/events.out.tfevents.9232
  
        /parent/directory/path/run3/
        /parent/directory/path/run3/events.out.tfevents.9232
  x = EventMultiplexer().AddRunsFromDirectory('/parent/directory/path')
  (which is equivalent to:)
  x = EventMultiplexer({'run1': '/parent/directory/path/run1', 'run2':...}
  ```
  
  If you would like to watch `/parent/directory/path`, wait for it to be created
    (if necessary) and then periodically pick up new runs, use
    `AutoloadingMultiplexer`
  @@Tensors
docstr: Returns EventAccumulator for a given run.
  
  Args:
    run: String name of run.
  
  Returns:
    An EventAccumulator object.
  
  Raises:
    KeyError: If run does not exist.
docstr_tok: ["Returns","EventAccumulator","for","a","given","run",".","Args",":","run",":","String","name","of","run",".","Returns",":","An","EventAccumulator","object",".","Raises",":","KeyError",":","If","run","does","not","exist","."]
code: def GetAccumulator(self, run):
      """Returns EventAccumulator for a given run.
  
      Args:
        run: String name of run.
  
      Returns:
        An EventAccumulator object.
  
      Raises:
        KeyError: If run does not exist.
      """
      with self._accumulators_mutex:
          return self._accumulators[run]
  
imports: import os
  import queue
  import threading
  from typing import Optional
  from tensorboard.backend.event_processing import directory_watcher
  from tensorboard.backend.event_processing import plugin_event_accumulator
  from tensorboard.backend.event_processing import io_wrapper
  from tensorboard.util import tb_logging
label_desc: Use image node_id 20 for calling the EventMultiplexer obj's underlying member method code with example usage: obj.GetAccumulator(run) and returns: self